Disk drives are well known in the computer art for providing secondary mass storage with random access. A disk drive essentially comprises one or more magnetic data storage disks rotating on a spindle by a spindle motor, within an enclosed housing. A magnetic transducer head is placed on an actuator arm and positioned very closely to each data storage surface by a slider suspended upon an air bearing. Servo information are typically written in servo sectors which interrupt data sectors or blocks on each disk. Servo information provide a servo control loop in the disk drive with head position information to enable a head positioner mechanism, such as a rotary voice coil motor, to move the actuator, and therefore the head, from track to track during random access track seeking operations, and to maintain the head in proper alignment with a track centerline during track following operations when user data is written to or read from the available data block storage areas of the disk surface.
There have been efforts to increase the data storage capacity of disk drives. In one example, the bit density on the disk magnetic medium is increased to pack the data more tightly on a given track. When this method is implemented, writing data is still relatively simple using standard inductive head technology. Reading the data back, however, becomes more challenging as spacing between flux transitions on the medium is reduced. To ensure accurate data reads, several methods are used. It is known to use two separate heads; one for reading and one for writing. Standard inductive heads are used for writing, and ultra-sensitive magneto-resistive (MR) heads are used for reading. The read heads generate analog signals in response to flux transitions on the medium, and the “read signal” is interpreted or “detected” by the drive electronics (e.g., detector).
Sampled data detectors implementing partial response (PR) signaling are in use in conventional disk drives. In sampled data detection systems, the readback signal is filtered and sampled at a channel rate of 1/T, wherein T is the duration of a channel symbol. One such technique employs what is known as a partial response maximum likelihood (PRML) system. The synchronous sampling process employed in PRML quantizes signal amplitudes at specific intervals throughout each readback signal transition interval T. One widespread PRML system uses filters to equalize the readback signal to a partial response class 4 (PR4) signal. The discrete-time transfer function of a PR4 channel is (1−D)2, where D represents a unit-time delay operator with unit-time T. In an idealized PR4 channel, a noiseless output is equal to the input signal minus a version of the input signal delayed in time by 2T. In a practical PR4 channel, the output of the noisy partial response channel is sampled at the channel rate and detected using a sequence detector, such as a Viterbi detector. Typically, the Viterbi detector is designed for maximum-likelihood detection of the sampled partial response channel in additive, independent, and identically distributed Gaussian noise with zero mean. Another partial response model is EPR4 with a discrete-time transfer function of (1−D)(1+D)2 or (1+D−D2−D3) and EEPR4 with a discrete-time transfer function of (1−D)(1+D)3 or (1+2D−2D3−D4). Other partial response models are also known, such as new partial response (NPR) having a unit pulse response of e.g. 7+4D−4D2−5D3−2D4.
Once a channel model is selected, a sequence detector may be fashioned. Sequence detectors frequently implement a version of the Viterbi algorithm. A Viterbi detector implementing the Viterbi algorithm minimizes squared Euclidean distance between the sequence of noisy samples and all possible sequences of idealized noiseless samples in accordance with the particular channel model. The Viterbi algorithm is an iterative process of keeping track of the path (branch) with the smallest accumulated metric (branch metric BM) leading to each state. The metrics of all of the paths leading into a particular state are calculated and compared. Then, the path with the smallest metric is selected as a survivor path and the other paths are discarded. In this manner all paths which are not part of the minimum metric path are systematically eliminated. The survivor path to each state is stored in a path memory. Given that the path memory is made sufficiently long, all of the selected survivor paths will diverge from a single path within the span of the path memory. The single path from which all the current survivor paths diverge is the minimum metric path. The Viterbi detector then traces back along the path memory to find the convergence state. The input sequence associated with the single minimum metric path then becomes the most-likely symbol output of the Viterbi detector.
A Viterbi detector does not attempt to decide whether a transition has occurred upon receipt of a readback sample or samples taken from a particular transition. Rather, samples are taken from the readback signal and equalized to the target channel model. The Viterbi detector then keeps a running tally of the error between the actual sample sequence and a correct sample sequence, i.e. a sequence that would be expected if the recording medium had been written with a particular sequence of transitions. One way of visualizing the Viterbi detector path memory is by way of a trellis diagram having plural states and plural paths leading from each state to other states. As analog-to-digital samples (s) are fed into one end of the trellis, estimates of previous bits are put out at an opposite end of the trellis. An error metric is determined for each one of plural possible state transition sequences. As more samples come into the Viterbi detector, less probable transition sequences (branches/paths) are eliminated, and by tracing back along the trellis a most likely path emerges as a convergent set of paths and enables a most-likely data decision to be made by the Viterbi detector.
In its current implementation, PRML presents limitations. It is generally known that for any given magnetic recording product, variations exist in the actual head/media response on a per head/disk basis as a function of many parameters, including manufacturing tolerances on the head and disk components, fly height of the magnetic head, component aging, environmental conditions, radius of the particular track, etc. This variation manifests itself mainly in pulse width and signal-to-noise-ratio variations.
Because of the variations, the optimum partial response target varies over the range of heads and disks, head aging, etc. The proper (e.g., optimal) partial response target would be one that adapts as a function of channel variation to jointly improve said minimum-distance between all allowable sequences of idealized channel outputs (ICO's), while also minimizing the noise and equalization error for said Viterbi detector error events (known as “sigma”). Further, conventional algorithms do not provide for automatically optimizing over different types of Viterbi detector error events There are many parameters and algorithms in a read channel utilizing a Viterbi-type detector that are a function of the PR target coefficients. In order for the read channel to work reliably, these parameters and algorithms should be adjusted properly based on the PR target coefficients selected. Previous approaches provide only one or a small number of fixed (i.e., hardcoded) PR targets.